Differentiation of cocoa nibs from distinct origins using comprehensive two-dimensional gas chromatography and multivariate analysis

Solid-phase microextraction coupled to comprehensive multidimensional gas chromatography for food analysis

Solid-phase microextraction and comprehensive multidimensional gas chromatography represent two milestone innovations that occurred in the field of separation science in the 1990s. They have a common root in their introduction and have found a perfect coupling in their evolution and applications. This review will focus on food analysis, where the paradigm has changed significantly over time, moving from a targeted analysis, focusing on a limited number of analytes at the time, to a more holistic approach for assessing quality in a larger sense. Indeed, not only some major markers or contaminants are considered, but a large variety of compounds and their possible interaction, giving rise to the field of foodomics. In order to obtain such detailed information and to answer more sophisticated questions related to food quality and authenticity, the use of SPME-GC × GC-MS has become essential for the comprehensive analysis of volatile and semi-volatile analytes. This article provides a critical review of the various applications of SPME-GC × GC in food analysis, emphasizing the crucial role this coupling plays in this field. Additionally, this review dwells on the importance of appropriate data treatment to fully harness the results obtained to draw accurate and meaningful conclusions.

Identification of geographical origins of soybean pastes using headspace gas chromatography-mass spectrometry by selecting sample-descriptive components with an Incremental Association Markov Blanket

The identification of geographical origins of soybean pastes using headspace gas chromatography-mass spectrometry was attempted in this study. Since soybean paste was odor-rich, 36 components were identified in the imported and domestic soybean samples. t-Test, variable importance in projection (VIP), and Incremental Association Markov Blanket (IAMB) were employed to select proper components that could effectively discriminate the two sample groups. The discrimination accuracies were below 87.3 % when all 36 components were fed for either LDA, k-NN, or SVM. When the five t-test-selected components or six VIP score-selected components were employed, the accuracies improved to 95.2-96.2 %. The IAMB selected three different components were 3-methylbutanal, 4-methylnonane, and 2,3-pentanedione, and the correlations among their peak areas were not significant. This suggests that these three components were independently relevant for the discrimination. The accuracy obtained using these three components was superior, 97.7 %, as undescriptive and/or redundant components for the discrimination were excluded.

A review of conventional and rapid analytical techniques coupled with multivariate analysis for origin traceability of soybean

Soybean has developed a reputation as a superfood due to its nutrient profile, health benefits, and versatility. Since 1960, its demand has increased dramatically, going from a mere 17 MMT to almost 358 MMT in the production year 2021/22. These extremely high production rates have led to lower-than-expected product quality, adulteration, illegal trade, deforestation, and other concerns. This necessitates the development of an effective technology to confirm soybean's provenance. This is the first review that investigates current analytical techniques coupled with multivariate analysis for origin traceability of soybeans. The fundamentals of several analytical techniques are presented, assessed, compared, and discussed in terms of their operating specifics, advantages, and shortcomings. Additionally, significance of multivariate analysis in analyzing complex data has also been discussed.

Amazonian cacao-clone nibs discrimination using NIR spectroscopy coupled to naïve Bayes classifier and a new waveband selection approach

Near-Infrared Spectroscopy (NIRS) has shown to be helpful in the study of rice, tea, cocoa, and other foods due to its versatility and reduced sample treatment. However, the high complexity of the data produced by NIR sensors makes necessary pre-treatments such as feature selection techniques that produce compact profiles. Supervised and unsupervised techniques have been tested, creating different subsets of features for classification, which affect the performance of the classifiers based on such compact profiles. In this sense, we propose and test a new covering array feature selection (CAFS) algorithm coupled to the naïve Bayes classifier (NBC) to discriminate among Amazonian cacao nibs from six cacao clones. The CAFS wrapper approach looks for the wavebands that maximize the F₁-score, and then, are more relevant for classification. For this purpose, cacao pods of six varieties were collected, and their grains were extracted and processed (fermented, dried, roasted, and milled) to obtain cacao nibs. Then from each clone NIR spectral profiles in the range of 1100-2500 nm were extracted, and relevant wavebands were selected using the proposed CAFS algorithm. For comparison, two standard feature selection techniques were implemented the multi-cluster feature selection MCFS and the eigenvector centrality feature selection ECFS. Then, based on the different selected variables, three NBCs were built and compared among them through statistical metrics. The results showed that using the wavebands selected by CAFS, the NBC performed an average accuracy of 99.63%; being this superior to the 94.92% and 95.79% for ECFS and MCFS respectively. These results showed that the wavebands selected by the proposed CAFS algorithm allowed obtaining a better fit concerning other feature selection methods reported in the literature.

Cocoa origin classifiability through LC-MS data: A statistical approach for large and long-term datasets

Classification of food samples based upon their countries of origin is an important task in food industry for quality assurance and development of fine flavor products. Liquid chromatography -mass spectrometry (LC-MS) provides a fast technique for obtaining in-depth information about chemical composition of foods. However, in a large dataset that is gathered over a period of few years, multiple, incoherent and hard to avoid sources of variations e.g., experimental conditions, transportation, batch and instrumental effects, etc. pose technical challenges that make the study of origin classification a difficult problem. Here, we use a large dataset gathered over a period of four years containing 297 LC-MS profiles of cocoa sourced from 10 countries to demonstrate these challenges by using two popular multivariate analysis methods: principal component analysis (PCA) and linear discriminant analysis (LDA). We show that PCA provides a limited separation in bean origin, while LDA suffers from a strong non-linear dependence on the set of compounds. Further, we show for LDA that a compound selection criterion based on Gaussian distribution of intensities across samples dramatically enhances origin clustering of samples thereby suggesting possibilities for studying marker compounds in such a disparate dataset through this approach. In essence, we show and develop a new approach that maximizes, avoiding overfitting, the utility of multivariate analysis in a highly complex dataset.

Analysis of the molecular heterogeneity of poly(lactic acid)/poly(butylene succinate-co-adipate) blends by hyphenating size exclusion chromatography with nuclear magnetic resonance and infrared spectroscopy

The compositional and stereochemical heterogeneity of copolymers are key molecular metrics, and their knowledge is of pivotal importance for evidence based material development. Yet, while it is state of the art to determine these parameters for many petroleum based polymers, little insight exists in that regard for bio-based materials. Towards this end, size exclusion chromatography (SEC) was hyphenated with nuclear magnetic resonance spectroscopy (NMR) in an offline manner and a blend of poly(lactic acid) (PLA) and poly(butylene succinate-co-adipate) (PBSA) investigated. Thus, the microstructural heterogeneity could be shown with regard to tacticity of the PLA and regioregularity of the PBSA component. The results show, that the highest molar mass fraction differs in stereochemical composition from the others. It may be assumed that this is the result of misinsertions with regard to stereochemistry occurring during the catalytic polymerization of the lactide. While the content of both constituent polymers along the molar mass axis could be well studied using a univariate analysis of the infrared (IR) spectra, this method failed to profile the adipate and succinate content individually. For this purpose, SEC was coupled to IR spectroscopy in online mode and the spectra were evaluated by a multivariate protocol. Thus, the content of each monomer along the molar mass distribution could be mapped with high chromatographic resolution.

Recent applications of chemometrics in one- and two-dimensional chromatography

The proliferation of increasingly more sophisticated analytical separation systems, often incorporating increasingly more powerful detection techniques, such as high-resolution mass spectrometry, causes an urgent need for highly efficient data-analysis and optimization strategies. This is especially true for comprehensive two-dimensional chromatography applied to the separation of very complex samples. In this contribution, the requirement for chemometric tools is explained and the latest developments in approaches for (pre-)processing and analyzing data arising from one- and two-dimensional chromatography systems are reviewed. The final part of this review focuses on the application of chemometrics for method development and optimization.

Chemometric approaches for postharvest quality tracing of cocoa: An efficient method to distinguish plant material origin

The aim of this study was to compare the quality of a mixture of cocoa harvested and fermented in three subregions of Antioquia (Colombia), from the chemometric profile based on multivariate statistical analysis. A mixture of clones CCN-52, ICS-1, FLE-2, and FEC-2 harvested in Bajo Cauca, Uraba and Magdalena Medio were subjected to a spontaneous fermentation. The characterization of raw and well-fermented cocoa was performed through 38 parameters, and results were compared by a Principal Component Analysis (PCA) and a Cluster Analysis (CA), followed by a Principal Factors Analysis (PFA- CA). The CA showed that there are differences among subregions only in raw cocoa from Bajo Cauca. PCA allowed identifying the variability between raw and fermented cocoa in a representative way and these results were consistent with the chemical profile. Besides, the number of parameters to differentiate raw cocoa from different subregions was reduced (11–13 parameters) and it was possible to characterize well fermented cocoa with only 10 parameters of 38. PFA-CA consolidated in three factors a grouping to identify the cocoa quality according to the process or interest of the sensory or functional properties. Factor 1 (cocoa quality indicators with functional properties), Factor 2 (indicators of quality of the beginning of fermentation) and Factor 3 (indicators of quality of well-fermented cocoa) each one with a weight of 39, 35 and 26 respectively.

Assessment of volatile fingerprint by HS-SPME/GC-qMS and E-nose for the classification of cocoa bean shells using chemometrics

The cocoa bean shell (CBS) is a main by-product of cocoa processing, with great potential to be used as an ingredient for functional foods because of its nutritional and flavour properties. This study aimed to characterise and classify CBSs obtained from cocoa beans of diverse cultivars and collected in different geographical origins through their volatile profile assessed using headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME/GC-qMS) and E-nose combined with principal component analysis (PCA). The study provides, for the first time in a representative set of samples, a comprehensive fingerprint and semi-quantitative data for >100 volatile organic compounds (VOCs), such as aldehydes, ketones, pyrazines, alcohols, and acids. Through PCA, a clear separation of the Criollo cultivar from the other cultivars was achieved with both GC-qMS and E-nose analytical techniques because of the high content of key-aroma VOCs. Several biomarkers identified by GC-qMS, such as 2-hepanol, 2-methylpropanoic acid, and 2,3,5-trimethylpyrazine, recognized as key-aroma compounds for cocoa beans, were found suitable for the classification of CBSs according to their quality and origin. GC-qMS and E-nose appeared to be suitable analytical approaches to classify CBSs, with a high correlation between both analytical techniques. The volatile fingerprint and classification of CBSs could allow for the selection of samples with a specific flavour profile according to the food application and, therefore, constitute an interesting approach to valorise this by-product as a food ingredient.

Strategies for Accurate Quantitation of Volatiles from Foods and Plant-Origin Materials: A Challenging Task

The volatile fraction of foods and of plant-origin materials provides functional information on sample-related variables, and gas-phase extractions are ideal approaches for its accurate chemical characterization. However, for gas-phase sampling, the usual procedures adopted to standardize results from solvent extraction methods are not appropriate: headspace (HS) composition depends on the intrinsic physicochemical analyte properties (volatility, polarity, partition coefficient(s)) and matrix effects. Method development, design, and expression of the results are therefore challenging. This review article focuses on volatile vapor-phase quantitation methods (internal standard normalization, standard addition, stable isotope dilution assay, multiple headspace extraction) and their suitability in different applications. Because of the analyte informative role, the different ways of expressing results (normalized chromatographic area, percent normalized chromatographic areas, and absolute concentrations) are discussed and critically evaluated with examples of quality markers in chamomile, process contaminants (furan and 2-methylfuran) in roasted coffee, and key-aroma compounds from high-quality cocoa.

Two-Dimensional Gas Chromatography Coupled With Mass Spectrometry in Food Analysis

The development of instrumental analytical techniques provided the opportunity for in-depth characterization of many food matrices. In particular, the use of gas chromatography coupled with mass spectrometry gives impressive results in terms of quality and authenticity testing, conducting food freshness evaluations and contamination assessments. A new variant of gas chromatography, namely two-dimensional gas chromatography (GC × GC), and various versions of mass spectrometry have been developed since last 15 years, and they still remain at the time of their renaissance. The present critical review is focused on the use of GC × GC coupled with mass spectrometry for qualitative and quantitative reasons in food analysis. It is explained how powerful analytical tool is above-mentioned technical solution. Special attention is devoted to the issues related to the development of this technique during last years in terms of key construction elements, such as modulators and MS detectors. Finally, the critical discussion on many various aspects including advantages and more important disadvantages, caused probable moderate interest of this solution, in food analytics is concerned.

Comprehensive Chemical Fingerprinting of High-Quality Cocoa at Early Stages of Processing: Effectiveness of Combined Untargeted and Targeted Approaches for Classification and Discrimination

This study investigates chemical information of volatile fractions of high-quality cocoa (Theobroma cacao L. Malvaceae) from different origins (Mexico, Ecuador, Venezuela, Columbia, Java, Trinidad, and Sao Tomè) produced for fine chocolate. This study explores the evolution of the entire pattern of volatiles in relation to cocoa processing (raw, roasted, steamed, and ground beans). Advanced chemical fingerprinting (e.g., combined untargeted and targeted fingerprinting) with comprehensive two-dimensional gas chromatography coupled with mass spectrometry allows advanced pattern recognition for classification, discrimination, and sensory-quality characterization. The entire data set is analyzed for 595 reliable two-dimensional peak regions, including 130 known analytes and 13 potent odorants. Multivariate analysis with unsupervised exploration (principal component analysis) and simple supervised discrimination methods (Fisher ratios and linear regression trees) reveal informative patterns of similarities and differences and identify characteristic compounds related to sample origin and manufacturing step.